Context Caching for IoT-Based Applications: Opportunities and Challenges

The Internet of Things (IoT) is growing at a rapid pace. Applications using IoT technologies have transformed many activities to be digitized enabling more productivity, economy, and quality of work. Context Management Platforms (CMPs) that unify heterogeneous streams of big IoT data and derive insights of a sensed environment (called context) using inferencing, massively enhance the smartness of IoT-based applications. Handling this massive scale of data and processing in an IoT-ecosystem for context-aware applications are both time and resource consuming, especially consid-ering the bottlenecks in network and processing resources. While traditional data caching is a time-proven technique to enable low-latency delivery of data items for a superior perceived user experience, work in context caching is extremely limited. Context information is different from typically discussed forms of data in many ways. Context-aware traditional data caching techniques have limited applicability to caching context information due to many unique challenges. These challenges can be categorized by features of context, context quality demands, techniques for caching con-text information, and context cache memory technologies. We categorically discuss each challenge in this article supported by real-world scenarios and experimental results. We contend that context caching is distinct from traditional data caching techniques and highlight the importance of con-text caching for time-critical, adaptive, context-aware applications. This article aims to demystify the unique research opportunities and challenges when developing context caching techniques for scale and efficiency objectives of CMPs and provide directions for future work.